Instructions to use Tech-Anis/Wearable-Activity-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Tech-Anis/Wearable-Activity-Classifier with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Tech-Anis/Wearable-Activity-Classifier") - Notebooks
- Google Colab
- Kaggle
File size: 930 Bytes
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tags:
- keras
- time-series-classification
- education
---
# Wearable Activity Classifier – Group ___
## Task
Classify a 100-step, one-feature sensor sequence into **Stationary**, **Walking**, or **Running**.
## Model selected
- Architecture: [CNN / SimpleRNN / LSTM / CNN+LSTM]
- Input shape: `(100, 1)`
- Output classes: 3
- Parameters: ______
## Training data
Synthetic signals generated in the class notebook. The dataset was designed for teaching and is not a real wearable benchmark.
## Evaluation
- Test accuracy: ______
- Training time in our run: ______ seconds
## Why we selected this model
[Write 2–4 sentences using evidence from your comparison.]
## Limitations
- Synthetic, simplified data
- One sensor feature only
- No testing across real users/devices
- Not intended for health, safety, or production use
## Team learning note
[State one thing your group learned by comparing CNN, RNN, and LSTM.]
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